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A Data-Driven Support System for Aircraft Trajectory Prediction in the National Airspace System Open Access

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Although a recent audit report from the U.S. Department of Transportation shows declining flight delays over the last decade, scheduled U.S, passenger airlines still accrued 92 million system delay minutes that were estimated to result in $7.2 billion in direct aircraft operating costs in 2012. To address these flight delays, the Federal Aviation Administration (FAA) is implementing the Next Generation Air Transportation System (NextGen) which aims to transform air traffic operations to meet future growth. A core component of NextGen is Trajectory Based Operations (TBO), with goals that include improving throughput, flight efficiency, flight times, and schedule predictability through better prediction and coordination of aircraft trajectories in the National Airspace System (NAS). In this research, a novel approach is presented by constructing a Dynamic Bayesian Network (DBN) to accurately quantify delay uncertainty for airport origin-destination (OD) pairs. Since the size of the conditional probability tables (CPTs) grows exponentially as the number of variables increase in the DBN, parameter learning was developed within the Hadoop MapReduce distributed computing framework. Hadoop aids in the mitigation of scaling concerns which significantly reduce the computational time necessary for air traffic decision support. Experiments are performed using a fused historical aircraft radar dataset that improves on current data limitations to dynamically predict the probability of a delay and its causal factor(s) for the strategic prediction horizon. The predictive performance of the model is evaluated by focusing on major OD pairs in the NAS, and the results show flight delay time was predicted accurately approximately 92% of the time for the two hour prediction horizon. Furthermore, the results from the delay model are integrated into a developed real-time trajectory predictor that recommends which route an aircraft should fly given both historical and real-time flight delay information combined with data related to the aircraft and the external environment. This research is the first known attempt that combines elements of systems engineering (SE), operations research (OR), and distributed computing concepts to derive a data-driven decision support system for air traffic decision makers under operational uncertainty.

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